The present disclosure relates generally to simulation modeling and more particularly to interpolation techniques used for time alignment of multiple simulation models.
Modern policy, planning, and investment decisions are often made in the context of a complex system. Making good policy and investment decisions requires not just the gathering, mining, statistical analysis, and visualization of data, but also the use of simulation models that can predict future behaviors. Simulation modeling has become a very important field recently because it allows for analysis of data and provides predictions and explanations relating to future outcomes of each alternate decision.
The design of useful and robust simulation models is complicated because high level decisions frequently require understanding of multilayered interactions relating to diverse systems across a great many domains and disciplines, in order to gain synergistic understanding of highly complex problems while avoiding unintended consequences of policy and investment decisions. Monolithic models of complex systems are usually difficult and expensive to build, verify, validate, and maintain, and a more successful approach is to compose many different individual models across a wide variety of disciplines. Although better than a monolithic approach, collaborative modeling and simulation is far from trivial. The individual models are often pre-existing and heterogeneous, having been created by domain experts who have different worldviews and vocabularies, sit in different organizations, and have invested much effort in developing and implementing their models using different programming and development paradigms. A resulting complication is that collaborative simulation modeling requires the exchange of large-scale, high-resolution data generated by both real-world and simulated processes, and must be handled in an efficient and scalable manner. It is often the case that some large number of real or simulated data measurements (source data) are available for exchange, but a potential consumer of the data needs measurements at a different set of times or locations from what is available to form the desired set of target data. Frequently, the source data are available at irregular time points—as is typical with data produced by stochastic discrete-event simulation models—or at irregular points in space, whereas the data consumer needs the target data to be given at regular intervals in space or time. Even when the data are available at regular time points, the source data may be coarser than the desired target data, or the sets of source and target time points may not line up. These mismatch problems can arise more generally in other data-exchange or data-integration settings, such as data warehousing.